Double Mobility: Coverage of the Sea Surface with Mobile Sensor Networks
Bibliographic record
Abstract
We are interested in the sensor networks for scientific applications to cover and measure statistics on the sea surface. Due to flows and waves, the sensor nodes may gradually lose their positions; leaving the points of interest uncovered. Manual readjustment is costly and cannot be performed in time. We argue that a network of mobile sensor nodes which can perform self-adjustment is the best candidate to maintain the coverage of the surface area. In our application, we face a unique double mobility coverage problem. That is, there is an uncontrollable mobility, U-Mobility, by the flows which breaks the coverage of the sensor network. Moreover, there is also a controllable mobility, C-Mobility, by the mobile nodes which we can utilize to reinstall the coverage. Our objective is to build an energy efficient scheme for the sensor network coverage issue with this double mobility behavior. A key observation of our scheme is that the motion of the flow is not only a curse but should also be considered as a fortune. The sensor nodes can be pushed by free to some locations that potentially help to improve the overall coverage. With that taken into consideration, more efficient movement decision can be made. To this end, we present a dominating set maintenance scheme to maximally exploit the U-Mobility and balance the energy consumption among all the sensor nodes. We prove that the coverage is guaranteed in our scheme. We further propose a fully distributed protocol that addresses a set of practical issues. Through extensive simulation, we demonstrate that the network lifetime can be significantly extended, compared to a straight forward back-to-original reposition scheme.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".